Uncertainty-Informed Threshold Assessment of Model-Based Fault Detection for Modular Multilevel Converters

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Abstract
Determining threshold values in model based fault detection (MBFD) is a longstanding challenge, which is often addressed through empirical and ambiguous adjustments. To tackle this issue, this paper proposes an uncertainty-informed framework for quantitative threshold assessment. The framework comprises three stages: 1) identifying uncertainties by explicitly understanding the implemented MBFD method, 2) quantifying fault detection residual through uncertainty propagation, and 3) determining and optimizing threshold values based on the quantified misdiagnosis rates. To validate the effectiveness of the proposed approach, a case study of a modular multilevel converter is selected. The proposed method not only enables a quantified threshold assessment but also enhances the robustness of the fault detection by accounting for uncertainties.
Funding Information
  • National Key Research and Development Program of China (2022YFE0196300)
  • National Natural Science Foundation of China (52207191)
  • Nanjing Science and Technology Innovation Project for Overseas Students (4216002303)
  • Start-up Research Fund of Southeast University (RF1028623299)

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